Bottom Line:
Data is often not provided in a biologically significant way for cross-analysis and -comparison, thus limiting its application.Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.This software and user manual are freely available at ftp://sage@bio.kuas.edu.tw/Extract-SAGE.zip.

Affiliation: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Taiwan.

ABSTRACT

Unlabelled: Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross-analysis and -comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross-analyzing gene expression data among different SAGE libraries of tissue sources; up- or down-regulated tissue-specific tags can be identified computationally. Extract-SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.

Availability: This software and user manual are freely available at ftp://sage@bio.kuas.edu.tw/Extract-SAGE.zip.

Figure 1: Screenshot of Extract‐SAGE. (A) The main window. Demonstration of (B) cross-analysis result, (C) tag to gene results, and (D) extract result using GA.

Mentions:
Figure 1 shows three functions provided by Extract‐SAGE, i.e. 1) cross‐analysis, 2) tag to gene, and 3) reducing‐analysis (using GA). The “cross‐analysis” function provides significant genes extracted by setting some operation conditions and difference factors between samples or sample groups of interest. Two output results, a tabular and graphic form, are provided. Both of them contain tag expression (tag per million, tpm) information of each group, and can be sorted based on the expression in the selected group or the expression difference between two selected groups. The graphic visualization of the results in gradient colors for the tag count in various samples is convenient for selecting gene candidates of interest. Tags with high or low expression (tpm) are easy to identify, and a set of key tags of curative or pathogenic genes is also provided. Users can submit a tag sequence with the “tag to gene” function to retrieve the corresponding information between tags and genes.

Figure 1: Screenshot of Extract‐SAGE. (A) The main window. Demonstration of (B) cross-analysis result, (C) tag to gene results, and (D) extract result using GA.

Mentions:
Figure 1 shows three functions provided by Extract‐SAGE, i.e. 1) cross‐analysis, 2) tag to gene, and 3) reducing‐analysis (using GA). The “cross‐analysis” function provides significant genes extracted by setting some operation conditions and difference factors between samples or sample groups of interest. Two output results, a tabular and graphic form, are provided. Both of them contain tag expression (tag per million, tpm) information of each group, and can be sorted based on the expression in the selected group or the expression difference between two selected groups. The graphic visualization of the results in gradient colors for the tag count in various samples is convenient for selecting gene candidates of interest. Tags with high or low expression (tpm) are easy to identify, and a set of key tags of curative or pathogenic genes is also provided. Users can submit a tag sequence with the “tag to gene” function to retrieve the corresponding information between tags and genes.

Bottom Line:
Data is often not provided in a biologically significant way for cross-analysis and -comparison, thus limiting its application.Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.This software and user manual are freely available at ftp://sage@bio.kuas.edu.tw/Extract-SAGE.zip.

Affiliation:
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Taiwan.

ABSTRACT

Unlabelled: Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross-analysis and -comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross-analyzing gene expression data among different SAGE libraries of tissue sources; up- or down-regulated tissue-specific tags can be identified computationally. Extract-SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.

Availability: This software and user manual are freely available at ftp://sage@bio.kuas.edu.tw/Extract-SAGE.zip.